计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 186-190.doi: 10.11896/j.issn.1002-137X.2018.08.033
汪琳娜1,2, 杨新3, 杨习贝4
WANG Lin-na1,2, YANG Xin3, YANG Xi-bei4
摘要: 传统单一阈值的邻域粗糙集不能有效降低信息的不确定性。考虑对象已有或预测的类别标签信息,通过引入类内和类间两种阈值,提出一种新的邻域粒化方法,并构建了一种基于监督邻域的粗糙集模型。该模型是传统邻域粗糙集的推广形式。通过分析双阈值下的邻域粒子变化规律,给出该模型的粗糙近似质量和条件熵单调性变化定理。最后通过4个UCI数据集验证了该模型的性能。实验结果显示,可以通过调节监督阈值参数来改善论域的邻域粒化效果,并降低信息的不确定性。
中图分类号:
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